Threshold computing in a distributed computing system

ABSTRACT

A computing device includes an interface configured to interface and communicate with a dispersed storage network (DSN), a memory that stores operational instructions, and a processing module operably coupled to the interface and memory such that the processing module, when operable within the computing device based on the operational instructions, is configured to perform various operations. The computing device selects a subset of the other computing devices to perform a computing task on a data object. The computing device determines processing parameters of the data and determines task partitioning. The computing device also processes the data based on processing parameters to generate data slice groupings and partitions the task based on the task partitioning to generate partial tasks. The computing device obtains and processes at least the decode threshold number of the plurality of partial results generated by the subset of the other computing devices to generate a result.

CROSS REFERENCE TO RELATED PATENTS

The present U.S. Utility Patent Application also claims prioritypursuant to 35 U.S.C. §120, as a continuation-in-part (CIP) of U.S.Utility patent application Ser. No. 13/865,641, entitled “DISPERSEDSTORAGE NETWORK SECURE HIERARCHICAL FILE DIRECTORY,” filed Apr. 18,2013, pending, which claims priority pursuant to 35 U.S.C. §120, as acontinuation-in-part (CIP) of U.S. Utility patent application Ser. No.13/707,490, entitled “RETRIEVING DATA FROM A DISTRIBUTED STORAGENETWORK,” filed Dec. 6, 2012, now issued as U.S. patent 9,304,857 onApr. 5, 2016, which claims priority pursuant to 35 U.S.C. §119(e) toU.S. Provisional Application No. 61/569,387, entitled “DISTRIBUTEDSTORAGE AND TASK PROCESSING,” filed Dec. 12, 2011, expired, all of whichare hereby incorporated herein by reference in their entirety and madepart of the present U.S. Utility Patent Application for all purposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND OF THE INVENTION

Technical Field of the Invention

This invention relates generally to computer networks and moreparticularly to dispersing error encoded data.

Description of Related Art

Computing devices are known to communicate data, process data, and/orstore data. Such computing devices range from wireless smart phones,laptops, tablets, personal computers (PC), work stations, and video gamedevices, to data centers that support millions of web searches, stocktrades, or on-line purchases every day. In general, a computing deviceincludes a central processing unit (CPU), a memory system, userinput/output interfaces, peripheral device interfaces, and aninterconnecting bus structure.

As is further known, a computer may effectively extend its CPU by using“cloud computing” to perform one or more computing functions (e.g., aservice, an application, an algorithm, an arithmetic logic function,etc.) on behalf of the computer. Further, for large services,applications, and/or functions, cloud computing may be performed bymultiple cloud computing resources in a distributed manner to improvethe response time for completion of the service, application, and/orfunction. For example, Hadoop is an open source software framework thatsupports distributed applications enabling application execution bythousands of computers.

In addition to cloud computing, a computer may use “cloud storage” aspart of its memory system. As is known, cloud storage enables a user,via its computer, to store files, applications, etc. on an Internetstorage system. The Internet storage system may include a RAID(redundant array of independent disks) system and/or a dispersed storagesystem that uses an error correction scheme to encode data for storage.

Prior art data storage systems do not have inability to execute dataprocessing operations in a fully effective or efficient manner. Forexample, the prior art does not provide adequate means by whichappropriate resources can be used and effectively or efficientlyleverage to ensure a best use thereof. There continues to be much roomfor improvement for identifying better and improved means for executionof data processing operations.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a dispersed ordistributed storage network (DSN) in accordance with the presentinvention;

FIG. 2 is a schematic block diagram of an embodiment of a computing corein accordance with the present invention;

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data in accordance with the present invention;

FIG. 4 is a schematic block diagram of a generic example of an errorencoding function in accordance with the present invention;

FIG. 5 is a schematic block diagram of a specific example of an errorencoding function in accordance with the present invention;

FIG. 6 is a schematic block diagram of an example of a slice name of anencoded data slice (EDS) in accordance with the present invention;

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of data in accordance with the present invention;

FIG. 8 is a schematic block diagram of a generic example of an errordecoding function in accordance with the present invention;

FIG. 9 is a logic diagram of an example of a method for outbound DSTprocessing in accordance with the present invention;

FIG. 10 is a flowchart illustrating an example of storing and processinga group of slices;

FIG. 11 is a flowchart illustrating an example of initiating thresholdcomputing in accordance with the invention; and

FIG. 12 is a flowchart illustrating an example of processing a thresholdcomputing task in accordance with the invention.

DETAILED DESCRIPTION OF THE INVENTION

FIG. 1 is a schematic block diagram of an embodiment of a dispersed, ordistributed, storage network (DSN) 10 that includes a plurality ofcomputing devices 12 -16, a managing unit 18, an integrity processingunit 20, and a DSN memory 22. The components of the DSN 10 are coupledto a network 24, which may include one or more wireless and/or wirelined communication systems; one or more non-public intranet systemsand/or public internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The DSN memory 22 includes a plurality of storage units 36 that may belocated at geographically different sites (e.g., one in Chicago, one inMilwaukee, etc.), at a common site, or a combination thereof. Forexample, if the DSN memory 22 includes eight storage units 36, eachstorage unit is located at a different site. As another example, if theDSN memory 22 includes eight storage units 36, all eight storage unitsare located at the same site. As yet another example, if the DSN memory22 includes eight storage units 36, a first pair of storage units are ata first common site, a second pair of storage units are at a secondcommon site, a third pair of storage units are at a third common site,and a fourth pair of storage units are at a fourth common site. Notethat a DSN memory 22 may include more or less than eight storage units36. Further note that each storage unit 36 includes a computing core (asshown in FIG. 2, or components thereof) and a plurality of memorydevices for storing dispersed error encoded data.

Each of the computing devices 12-16, the managing unit 18, and theintegrity processing unit 20 include a computing core 26, which includesnetwork interfaces 30-33. Computing devices 12-16 may each be a portablecomputing device and/or a fixed computing device. A portable computingdevice may be a social networking device, a gaming device, a cell phone,a smart phone, a digital assistant, a digital music player, a digitalvideo player, a laptop computer, a handheld computer, a tablet, a videogame controller, and/or any other portable device that includes acomputing core. A fixed computing device may be a computer (PC), acomputer server, a cable set-top box, a satellite receiver, a televisionset, a printer, a fax machine, home entertainment equipment, a videogame console, and/or any type of home or office computing equipment.Note that each of the managing unit 18 and the integrity processing unit20 may be separate computing devices, may be a common computing device,and/or may be integrated into one or more of the computing devices 12-16and/or into one or more of the storage units 36.

Each interface 30, 32, and 33 includes software and hardware to supportone or more communication links via the network 24 indirectly and/ordirectly. For example, interface 30 supports a communication link (e.g.,wired, wireless, direct, via a LAN, via the network 24, etc.) betweencomputing devices 14 and 16. As another example, interface 32 supportscommunication links (e.g., a wired connection, a wireless connection, aLAN connection, and/or any other type of connection to/from the network24) between computing devices 12 & 16 and the DSN memory 22. As yetanother example, interface 33 supports a communication link for each ofthe managing unit 18 and the integrity processing unit 20 to the network24.

Computing devices 12 and 16 include a dispersed storage (DS) clientmodule 34, which enables the computing device to dispersed storage errorencode and decode data as subsequently described with reference to oneor more of FIGS. 3-8. In this example embodiment, computing device 16functions as a dispersed storage processing agent for computing device14. In this role, computing device 16 dispersed storage error encodesand decodes data on behalf of computing device 14. With the use ofdispersed storage error encoding and decoding, the DSN 10 is tolerant ofa significant number of storage unit failures (the number of failures isbased on parameters of the dispersed storage error encoding function)without loss of data and without the need for a redundant or backupcopies of the data. Further, the DSN 10 stores data for an indefiniteperiod of time without data loss and in a secure manner (e.g., thesystem is very resistant to unauthorized attempts at accessing thedata).

In operation, the managing unit 18 performs DS management services. Forexample, the managing unit 18 establishes distributed data storageparameters (e.g., vault creation, distributed storage parameters,security parameters, billing information, user profile information,etc.) for computing devices 12-14 individually or as part of a group ofuser devices. As a specific example, the managing unit 18 coordinatescreation of a vault (e.g., a virtual memory block associated with aportion of an overall namespace of the DSN) within the DSN memory 22 fora user device, a group of devices, or for public access and establishesper vault dispersed storage (DS) error encoding parameters for a vault.The managing unit 18 facilitates storage of DS error encoding parametersfor each vault by updating registry information of the DSN 10, where theregistry information may be stored in the DSN memory 22, a computingdevice 12-16, the managing unit 18, and/or the integrity processing unit20.

The DSN managing unit 18 creates and stores user profile information(e.g., an access control list (ACL)) in local memory and/or withinmemory of the DSN module 22. The user profile information includesauthentication information, permissions, and/or the security parameters.The security parameters may include encryption/decryption scheme, one ormore encryption keys, key generation scheme, and/or dataencoding/decoding scheme.

The DSN managing unit 18 creates billing information for a particularuser, a user group, a vault access, public vault access, etc. Forinstance, the DSN managing unit 18 tracks the number of times a useraccesses a non-public vault and/or public vaults, which can be used togenerate a per-access billing information. In another instance, the DSNmanaging unit 18 tracks the amount of data stored and/or retrieved by auser device and/or a user group, which can be used to generate aper-data-amount billing information.

As another example, the managing unit 18 performs network operations,network administration, and/or network maintenance. Network operationsincludes authenticating user data allocation requests (e.g., read and/orwrite requests), managing creation of vaults, establishingauthentication credentials for user devices, adding/deleting components(e.g., user devices, storage units, and/or computing devices with a DSclient module 34) to/from the DSN 10, and/or establishing authenticationcredentials for the storage units 36. Network administration includesmonitoring devices and/or units for failures, maintaining vaultinformation, determining device and/or unit activation status,determining device and/or unit loading, and/or determining any othersystem level operation that affects the performance level of the DSN 10.Network maintenance includes facilitating replacing, upgrading,repairing, and/or expanding a device and/or unit of the DSN 10.

The integrity processing unit 20 performs rebuilding of ‘bad’ or missingencoded data slices. At a high level, the integrity processing unit 20performs rebuilding by periodically attempting to retrieve/list encodeddata slices, and/or slice names of the encoded data slices, from the DSNmemory 22. For retrieved encoded slices, they are checked for errors dueto data corruption, outdated version, etc. If a slice includes an error,it is flagged as a ‘bad’ slice. For encoded data slices that were notreceived and/or not listed, they are flagged as missing slices. Badand/or missing slices are subsequently rebuilt using other retrievedencoded data slices that are deemed to be good slices to produce rebuiltslices. The rebuilt slices are stored in the DSN memory 22.

FIG. 2 is a schematic block diagram of an embodiment of a computing core26 that includes a processing module 50, a memory controller 52, mainmemory 54, a video graphics processing unit 55, an input/output (10)controller 56, a peripheral component interconnect (PCI) interface 58,an 10 interface module 60, at least one 10 device interface module 62, aread only memory (ROM) basic input output system (BIOS) 64, and one ormore memory interface modules. The one or more memory interfacemodule(s) includes one or more of a universal serial bus (USB) interfacemodule 66, a host bus adapter (HBA) interface module 68, a networkinterface module 70, a flash interface module 72, a hard drive interfacemodule 74, and a DSN interface module 76.

The DSN interface module 76 functions to mimic a conventional operatingsystem (OS) file system interface (e.g., network file system (NFS),flash file system (FFS), disk file system (DFS), file transfer protocol(FTP), web-based distributed authoring and versioning (WebDAV), etc.)and/or a block memory interface (e.g., small computer system interface(SCSI), internet small computer system interface (iSCSI), etc.). The DSNinterface module 76 and/or the network interface module 70 may functionas one or more of the interface 30-33 of FIG. 1. Note that the 10 deviceinterface module 62 and/or the memory interface modules 66-76 may becollectively or individually referred to as 10 ports.

FIG. 3 is a schematic block diagram of an example of dispersed storageerror encoding of data. When a computing device 12 or 16 has data tostore it disperse storage error encodes the data in accordance with adispersed storage error encoding process based on dispersed storageerror encoding parameters. The dispersed storage error encodingparameters include an encoding function (e.g., information dispersalalgorithm, Reed-Solomon, Cauchy Reed-Solomon, systematic encoding,non-systematic encoding, on-line codes, etc.), a data segmentingprotocol (e.g., data segment size, fixed, variable, etc.), and per datasegment encoding values. The per data segment encoding values include atotal, or pillar width, number (T) of encoded data slices per encodingof a data segment i.e., in a set of encoded data slices); a decodethreshold number (D) of encoded data slices of a set of encoded dataslices that are needed to recover the data segment; a read thresholdnumber (R) of encoded data slices to indicate a number of encoded dataslices per set to be read from storage for decoding of the data segment;and/or a write threshold number (W) to indicate a number of encoded dataslices per set that must be accurately stored before the encoded datasegment is deemed to have been properly stored. The dispersed storageerror encoding parameters may further include slicing information (e.g.,the number of encoded data slices that will be created for each datasegment) and/or slice security information (e.g., per encoded data sliceencryption, compression, integrity checksum, etc.).

In the present example, Cauchy Reed-Solomon has been selected as theencoding function (a generic example is shown in FIG. 4 and a specificexample is shown in FIG. 5); the data segmenting protocol is to dividethe data object into fixed sized data segments; and the per data segmentencoding values include: a pillar width of 5, a decode threshold of 3, aread threshold of 4, and a write threshold of 4. In accordance with thedata segmenting protocol, the computing device 12 or 16 divides the data(e.g., a file (e.g., text, video, audio, etc.), a data object, or otherdata arrangement) into a plurality of fixed sized data segments (e.g., 1through Y of a fixed size in range of Kilo-bytes to Tera-bytes or more).The number of data segments created is dependent of the size of the dataand the data segmenting protocol.

The computing device 12 or 16 then disperse storage error encodes a datasegment using the selected encoding function (e.g., Cauchy Reed-Solomon)to produce a set of encoded data slices. FIG. 4 illustrates a genericCauchy Reed-Solomon encoding function, which includes an encoding matrix(EM), a data matrix (DM), and a coded matrix (CM). The size of theencoding matrix (EM) is dependent on the pillar width number (T) and thedecode threshold number (D) of selected per data segment encodingvalues. To produce the data matrix (DM), the data segment is dividedinto a plurality of data blocks and the data blocks are arranged into Dnumber of rows with Z data blocks per row. Note that Z is a function ofthe number of data blocks created from the data segment and the decodethreshold number (D). The coded matrix is produced by matrix multiplyingthe data matrix by the encoding matrix.

FIG. 5 illustrates a specific example of Cauchy Reed-Solomon encodingwith a pillar number (T) of five and decode threshold number of three.In this example, a first data segment is divided into twelve data blocks(D1-D12). The coded matrix includes five rows of coded data blocks,where the first row of X11-X14 corresponds to a first encoded data slice(EDS 1_1), the second row of X21-X24 corresponds to a second encodeddata slice (EDS 2_1), the third row of X31-X34 corresponds to a thirdencoded data slice (EDS 3_1), the fourth row of X41-X44 corresponds to afourth encoded data slice (EDS 4_1), and the fifth row of X51-X54corresponds to a fifth encoded data slice (EDS 5_1). Note that thesecond number of the EDS designation corresponds to the data segmentnumber.

Returning to the discussion of FIG. 3, the computing device also createsa slice name (SN) for each encoded data slice (EDS) in the set ofencoded data slices. A typical format for a slice name 60 is shown inFIG. 6. As shown, the slice name (SN) 60 includes a pillar number of theencoded data slice (e.g., one of 1-T), a data segment number (e.g., oneof 1-Y), a vault identifier (ID), a data object identifier (ID), and mayfurther include revision level information of the encoded data slices.The slice name functions as, at least part of, a DSN address for theencoded data slice for storage and retrieval from the DSN memory 22.

As a result of encoding, the computing device 12 or 16 produces aplurality of sets of encoded data slices, which are provided with theirrespective slice names to the storage units for storage. As shown, thefirst set of encoded data slices includes EDS 1_1 through EDS 5_1 andthe first set of slice names includes SN 1_1 through SN 5_1 and the lastset of encoded data slices includes EDS 1_Y through EDS 5_Y and the lastset of slice names includes SN 1_Y through SN 5_Y.

FIG. 7 is a schematic block diagram of an example of dispersed storageerror decoding of a data object that was dispersed storage error encodedand stored in the example of FIG. 4. In this example, the computingdevice 12 or 16 retrieves from the storage units at least the decodethreshold number of encoded data slices per data segment. As a specificexample, the computing device retrieves a read threshold number ofencoded data slices.

To recover a data segment from a decode threshold number of encoded dataslices, the computing device uses a decoding function as shown in FIG.8. As shown, the decoding function is essentially an inverse of theencoding function of FIG. 4. The coded matrix includes a decodethreshold number of rows (e.g., three in this example) and the decodingmatrix in an inversion of the encoding matrix that includes thecorresponding rows of the coded matrix. For example, if the coded matrixincludes rows 1, 2, and 4, the encoding matrix is reduced to rows 1, 2,and 4, and then inverted to produce the decoding matrix.

Note that various examples, embodiments, etc. of the invention asdescribed herein may be implemented using one or more dispersed ordistributed storage network (DSN) modules. In some examples, a DSNmodule includes a plurality of distributed storage and/or task (DST)execution units 36 (e.g., storage units (SUs), computing devices, etc.)that may be located at geographically different sites (e.g., one inChicago, one in Milwaukee, etc.). Each of the DST execution units isoperable to store dispersed error encoded data and/or to execute, in adistributed manner, one or more tasks on data. The tasks may be a simplefunction (e.g., a mathematical function, a logic function, an identifyfunction, a find function, a search engine function, a replace function,etc.), a complex function (e.g., compression, human and/or computerlanguage translation, text-to-voice conversion, voice-to-textconversion, etc.), multiple simple and/or complex functions, one or morealgorithms, one or more applications, etc.

FIG. 9 is a logic diagram of an example of a method 900 for outbound DSTprocessing that begins at a step 910 with the DST client modulereceiving data and one or more corresponding tasks. The method 900continues at a step 920 with the DST client module determining a numberof DST units to support the task for one or more data partitions. Forexample, the DST client module may determine the number of DST units tosupport the task based on the size of the data, the requested task, thecontent of the data, a predetermined number (e.g., user indicated,system administrator determined, etc.), available DST units, capabilityof the DST units, and/or any other factor regarding distributed taskprocessing of the data. The DST client module may select the same DSTunits for each data partition, may select different DST units for thedata partitions, or a combination thereof

The method 900 continues at a step 930 with the DST client moduledetermining processing parameters of the data based on the number of DSTunits selected for distributed task processing. The processingparameters include data partitioning information, DS encodingparameters, and/or slice grouping information. The data partitioninginformation includes a number of data partitions, size of each datapartition, and/or organization of the data partitions (e.g., number ofdata blocks in a partition, the size of the data blocks, and arrangementof the data blocks). The DS encoding parameters include segmentinginformation, segment security information, error information, slicinginformation, and/or per slice security information. The slice groupinginformation includes information regarding how to arrange the encodeddata slices into groups for the selected DST units. As a specificexample, if, the DST client module determines that five DST units areneeded to support the task, then it determines that the error encodingparameters include a pillar with the five and a decode threshold ofthree.

The method 900 continues at a step 940 with the DST client moduledetermining task partitioning information (e.g., how to partition thetasks) based on the selected DST units and data processing parameters.The data processing parameters include the processing parameters and DSTunit capability information. The DST unit capability informationincludes the number of DT (distributed task) execution units, executioncapabilities of each DST execution unit (e.g., MIPS capabilities,processing resources (e.g., quantity and capability of microprocessors,CPUs, digital signal processors, co-processor, microcontrollers,arithmetic logic circuitry, and/or and the other analog and/or digitalprocessing circuitry), availability of the processing resources, memoryinformation (e.g., type, size, availability, etc.), and/or anyinformation germane to executing one or more tasks.

The method 900 continues at a step 950 with the DST client moduleprocessing the data in accordance with the processing parameters toproduce slice groupings. The method 900 continues at a step 960 with theDST client module partitioning the task based on the task partitioninginformation to produce a set of partial tasks. The method 900 continuesat a step 970 with the DST client module sending the slice groupings andthe corresponding partial tasks to the selected DST units.

FIG. 10 is a flowchart illustrating an example of storing and processinga group of slices. The method 1000 begins with a step 1010 where aprocessing module (e.g., of a distributed task (DT) execution module ofa distributed storage and task execution (DST EX) unit embedded within adisk drive unit) receives at least one partial task with regards to agroup of slices of contiguous data (e.g., from a DST client module). Themethod 1000 continues at the step 1015 where the processing modulereceives slices of the group of slices to produce received slices. Themethod 1000 continues at the step 1020 where, when an interim thresholdnumber (e.g., a maximum number of bytes limited by an ingestion cachememory) of received slices has been received, the processing modulestreams the received slices to a memory device for storage therein. Notethat the memory device may be any type of memory device including anyone or more of a hard disk drive (HDD), a disc drive, a storage unit(SU), etc. as desired in various examples and embodiments. The streamingmay provide a write bandwidth system improvement for the group of slices(e.g., as the group of slices pertain to the contiguous data).

The method 1000 continues at the step 1025 (and step 1030) where theprocessing module determines whether to execute a partial task. Thedetermination may be based on one or more of comparing an amount of datareceived to a data threshold, a partial task type, task executionresource availability, and a task schedule. For example, the processingmodule determines to execute the partial task when data of the receivedslices can be processed in accordance with a partial task. The method1000 branches to the step 1050 where the processing module determinesexecution steps and schedule when the processing module determines toexecute the partial task. The method 1000 continues to the next step1035 when the processing module determines not to execute the partialtask.

The method 1000 continues at the next step 1035 where the processingmodule determines whether more slices are expected. The determinationmay be based on one or more of a contiguous data size indicator, aquery, a lookup, and a number of bytes received so far. The method 1000repeats back to the step 1015 where the processing module receivesslices of the group of slices to produce received slices when theprocessing module determines that there are more slices. The method 1000continues to the next step 1050 when the processing module determinesthat there are no more slices.

The method 1000 continues at the next step 1050 where the processingmodule determines execution steps and schedule. The determination may bebased on one or more of the at least one partial task, the data, aprevious task schedule, a schedule template, a task execution resourceavailability level, and a task execution requirement. The method 1000continues at the step 1055 where the processing module identifies aportion of the contiguous data for execution of one or steps of theexecution steps. The identifying includes matching the portion of thecontiguous data to the one or more steps of execution steps based on oneor more of a data type indicator associated with the portion, a datatype associated with or more steps, and a data available indicator.

The method 1000 continues at the step 1060 where the processing moduleretrieves the portion of the contiguous data from the memory device as adata stream. Again, note that the memory device may be any type ofmemory device including any one or more of a HDD, a disc drive, a SU,etc. as desired in various examples and embodiments. The retrievingincludes accessing the disk drive for multiple contiguous data bytes.The streaming may provide a read bandwidth system improvement for theportion of data. The method 1000 continues at the step 1065 where theprocessing module executes the steps in accordance with the schedule onthe portion of the contiguous data to produce a partial result. Forexample, the processing module executes a search partial task on theportion to produce a search partial result.

The method 1000 continues at the step 1070 where the processing moduledispersed storage error encodes the partial results produce a pluralityof sets of slices in accordance with dispersal parameters associatedwith one or more of the group of slices and the at least one partialtask. The method 1000 continues at the step 1075 where the processingmodule facilitates storing a plurality of sets of slices in a dispersedor distributed storage network (DSN). For example, the processing modulesends groups of slices to a DST EX unit, wherein the slices are of acommon pillar number when a storage method 1000 indicates dispersedstorage. As another example, the processing module sends groups ofslices to a DST EX unit, wherein the slices are of two or more pillarnumber when a storage method 1000 indicates distributed task storage toenable subsequent task execution on the partial result. In addition, theprocessing module may receive more slices for more execution steps.

In an example of operation and implementation, a computing deviceincludes an interface configured to interface and communicate with adispersed or distributed storage network (DSN), a memory that storesoperational instructions, and a processing module operably coupled tothe interface and memory such that the processing module, when operablewithin the computing device based on the operational instructions, isconfigured to perform various operations.

For example, the computing device is configured to determine capabilitylevels of a plurality of other computing devices. Then, the computingdevice is configured to select, based on the capability levels of aplurality of other computing devices, a subset of the plurality of othercomputing devices to perform a computing task on a data object. Notethat he data object is segmented into a plurality of data segments, anda data segment of the plurality of data segments is dispersed errorencoded in accordance with dispersed error encoding parameters toproduce a set of encoded data slices (EDSs). Also, the set of EDSs maybe distributedly stored among a plurality of storage units (SUs). Notealso that a decode threshold number of EDSs are needed to recover thedata segment, a read threshold number of EDSs provides forreconstruction of the data segment, and a write threshold number of EDSsprovides for a successful transfer of the set of EDSs from a first atleast one location in the DSN to a second at least one location in theDSN.

Then, the computing device is configured to determine processingparameters of the data based on a number of the subset of the pluralityof other computing devices. Then the computing device is configured todetermine task partitioning based on the subset of the plurality ofother computing devices, the processing parameters, and a thresholdcomputing parameter. Then, the computing device is configured to processthe data based on processing parameters to generate data slicegroupings.

The computing device is then configured to partition the task based onthe task partitioning to generate partial tasks. Then, the computingdevice is configured to transmit the partial tasks and the data slicegroupings respectively to the subset of the plurality of other computingdevices to be executed respectively by the subset of the plurality ofother computing devices to generate a plurality of partial results;

When the decode threshold number of the plurality of partial results isgenerated by the subset of the plurality of other computing devices andavailable as indicated by at least the write threshold number of thesubset of the plurality of other computing devices, the computing deviceis then configured to obtain at least the decode threshold number of theplurality of partial results and process the at least the decodethreshold number of the plurality of partial results to generate aresult.

In some examples, the computing device is configured to select, based onthe capability levels of a plurality of other computing devices, thesubset of the plurality of other computing devices to perform acomputing task on the data object based on comparing an amount of dataassociated with the data object received to a data threshold, a partialtask type, task execution resource availability, and/or a task schedule.

Also, in some examples, note that the threshold computing parameterincludes a decode threshold number of computing devices, a pillar widthnumber of computing devices, and/or a task redundancy requirement numberof computing devices to execute an identical partial task.

In even other examples, the computing device is configured to determinewhether the decode threshold number of the plurality of partial resultsis generated by the subset of the plurality of other computing devicesand available based on receiving a partial result from at least one ofthe subset of the plurality of other computing devices, receiving apartial result status from the at least one of the subset of theplurality of other computing devices, a query operation to the at leastone of the subset of the plurality of other computing devices,retrieving a partial result from the at least one of the subset of theplurality of other computing devices, and/or a comparison of a number ofpartial results to the decode threshold number.

Also, in some examples, note that the computing device is configured todecode the at least the decode threshold number of the plurality ofpartial results to generate the result.

Note that the computing device may be located at a first premises thatis remotely located from at least one SU of a plurality of SUs withinthe DSN. Also, note that the computing device may be of any of a varietyof types of devices as described herein and/or their equivalentsincluding a SU of any group and/or set of SUs within the DSN, a wirelesssmart phone, a laptop, a tablet, a personal computers (PC), a workstation, and/or a video game device. Note also that the DSN may beimplemented to include or be based on any of a number of different typesof communication systems including a wireless communication system, awire lined communication systems, a non-public intranet system, a publicinternet system, a local area network (LAN), and/or a wide area network(WAN).

FIG. 11 is a flowchart illustrating an example of initiating thresholdcomputing, which includes similar steps to FIG. 9. The method 1100begins with the step 1110 where a processing module (e.g., of adistributed storage and task (DST) client module) receives data and acorresponding task. The method 1100 continues at the step 1120 where theprocessing module selects one or more DST execution units for the taskbased on a capability level associated with each of the DST executionunits. The selecting includes one or more of determining a number of DSTexecution units and selecting the number of DST execution units based onone or more of an estimated distributed computing loading level, a DSTexecution unit capability indicator, a DST execution unit performanceindicator, a DST execution unit availability level indicator, a taskschedule, and a DST execution unit threshold computing capabilityindicator. For example, the processing module selects DST executionunits 1-8 when DST execution unit availability level indicators for DSTexecution units 1-8 compares favorably to an estimated distributedcomputing loading level. The method 1100 continues with the step 1130where the processing module determines processing parameters of the databased on a number of DST execution units.

The method 1100 continues at the step 1140 where the processing moduledetermines task partitioning based on the DST execution units, theprocessing parameters, and a threshold computing parameter. Thethreshold computing parameter includes one or more of a decode thresholdnumber of DST execution units (e.g., SUs, computing devices, etc.), awidth number of DST execution units (e.g., SUs, computing devices,etc.), and a task redundancy requirement (e.g., a number of DSTexecution units (e.g., SUs, computing devices, etc.) to execute anidentical partial task). For example, the processing module partitionsthe task evenly into five partial tasks to assign to five of eight DSTexecution units (e.g., SUs, computing devices, etc.) when the decodethreshold number is five and the width number is eight. The method 1100continues with similar steps of FIG. 9 where the processing moduleprocesses the data in accordance with the processing parameters toproduce slice groupings in step 1150 and partitions the task based onthe task partitioning to produce partial tasks in step 1160.

The method 1100 continues at the step 1170 where the processing modulesends the slice groupings and corresponding partial tasks to theselected DST execution units (e.g., SUs, computing devices, etc.). Themethod 1100 continues at the step 1180 where the processing moduledetermines whether a decode threshold number of partial results areavailable. The determining may be based on one or more of receiving apartial result, receiving a partial result status, a query, retrieving apartial result, and comparing a number of partial results to the decodethreshold. The method 1100 continues at the step 1190 where theprocessing module obtains at least the decode threshold number ofpartial results based on the determining whether the decode thresholdnumber of partial results are available. The obtaining includes one ormore of receiving a partial result, determining dispersed or distributedstorage network (DSN) addresses corresponding to the selected DSTexecution units (e.g., SUs, computing devices, etc.), generating atleast a decode threshold number of partial result requests, and sendingthe at least the decode threshold number of partial result requests tothe selected DST execution units (e.g., SUs, computing devices, etc.)utilizing the corresponding DSN addresses. The method 1100 continues atthe step 1194 where the processing module processes the decode thresholdnumber of partial results to produce a result. The processing includesat least one of aggregating the partial results and/or decoding thepartial results to produce the result.

FIG. 12 is a flowchart illustrating an example of processing a thresholdcomputing task, which includes similar steps to FIG. 10. The method 1200begins with the step 1210 where a processing module (e.g., of adistributed storage and task (DST) execution unit) receives at least onepartial task with regards to a group of slices of contiguous data. Themethod 1200 continues at the step 1215 where the processing modulereceives the group of slices. The method 1200 continues with similarsteps of FIG. 10 where the processing module determines execution stepsof schedule in step 1220, identifies a portion of the contiguous data instep 1225, and executes the steps in accordance with the schedule on theportion of the contiguous data to produce a partial result in step 1230.

The method 1200 continues at the step 1240 where the processing moduledetermines whether the partial result compares favorably to an expectedresult. The expected result includes one or more of a result wasproduced, the result was produced without computing errors (e.g., nodivide by zero, etc.), the result is within a predetermined favorablerange of results, and a result type of the result is of a predeterminedresult type. The method 1200 branches to the step 1250 where theprocessing module indicates that the partial result is favorable whenprocessing module determines that the partial result compares favorablyto the expected result. The method 1200 continues to the next step 1245when the processing module determines that the partial result comparesunfavorably to the expected result. The method 1200 continues at thenext step 1245 where the processing module modifies the execution stepsand schedule. The modifying includes one or more of establishing updatedsteps and/or schedule to address an unfavorable nature of the partialresult. The method 1200 loops back to the step 1225 where the processingmodule identifies the portion of the contiguous data. Alternatively, theprocess may end when reaching a limit of a number of loops and/orreceiving a cancel request.

The method 1200 continues at the step 1250 where the processing moduleindicates that the partial result is favorable when the processingmodule determines that the partial result compares favorably to theexpected result. For example, the processing module sends a resultstatus to a requesting entity that includes an indication that thepartial result is favorable. The method 1200 continues at the step 1255where the processing module generates a slice grouping of the partialresult. The method 1200 continues at the step 1260 where the processingmodule generates error coded data slice groupings modificationinformation based on the slice groupings of the partial result. Thegenerating may be based on one or more of a number of participatingpillars, the slice grouping, a previous slice grouping of the partialresult, an encoding matrix, an error coded data pillar number, and azero information gain slice building approach. Such a zero informationgain slice rebuilding approach is discussed in greater detail withreference to parent application related materials incorporated byreference herein above.

The method 1200 continues at step 1265 for the processing modulefacilitates storing the slice grouping in the DSN. For example, theprocessing module stores the slice grouping in a memory associated witha local (e.g., present) DST execution unit (e.g., SU, computing device,etc.). The method 1200 continues at the step 1270 where the processingmodule facilitates storing the error coded data slice groupingdefecation information in the DSN. For example, the processing modulesends a first error coded data slice grouping modification informationto a first DST execution unit (e.g., SU, computing device, etc.) and asecond error coded data slice grouping modification information to asecond DST execution unit (e.g., SU, computing device, etc.), whereinthe first and second DST execution units (e.g., SUs, computing devices,etc.) store error coded data slices corresponding to the slice grouping.The method 1200 continues at the step 1275 where the processing moduleindicates that the partial result is available. For example, theprocessing module sends a result status to the requesting entity thatincludes an indication that the partial result is available (e.g.,available in the DSN for retrieval).

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, audio, etc. any of which may generally be referred to as‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. Such an industry-accepted toleranceranges from less than one percent to fifty percent and corresponds to,but is not limited to, component values, integrated circuit processvariations, temperature variations, rise and fall times, and/or thermalnoise. Such relativity between items ranges from a difference of a fewpercent to magnitude differences. As may also be used herein, theterm(s) “configured to”, “operably coupled to”, “coupled to”, and/or“coupling” includes direct coupling between items and/or indirectcoupling between items via an intervening item (e.g., an item includes,but is not limited to, a component, an element, a circuit, and/or amodule) where, for an example of indirect coupling, the intervening itemdoes not modify the information of a signal but may adjust its currentlevel, voltage level, and/or power level. As may further be used herein,inferred coupling (i.e., where one element is coupled to another elementby inference) includes direct and indirect coupling between two items inthe same manner as “coupled to”. As may even further be used herein, theterm “configured to”, “operable to”, “coupled to”, or “operably coupledto” indicates that an item includes one or more of power connections,input(s), output(s), etc., to perform, when activated, one or more itscorresponding functions and may further include inferred coupling to oneor more other items. As may still further be used herein, the term“associated with”, includes direct and/or indirect coupling of separateitems and/or one item being embedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, and/or “processing unit” may be a singleprocessing device or a plurality of processing devices. Such aprocessing device may be a microprocessor, micro-controller, digitalsignal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, and/or processing unit may be, or furtherinclude, memory and/or an integrated memory element, which may be asingle memory device, a plurality of memory devices, and/or embeddedcircuitry of another processing module, module, processing circuit,and/or processing unit. Such a memory device may be a read-only memory,random access memory, volatile memory, non-volatile memory, staticmemory, dynamic memory, flash memory, cache memory, and/or any devicethat stores digital information. Note that if the processing module,module, processing circuit, and/or processing unit includes more thanone processing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,and/or processing unit implements one or more of its functions via astate machine, analog circuitry, digital circuitry, and/or logiccircuitry, the memory and/or memory element storing the correspondingoperational instructions may be embedded within, or external to, thecircuitry comprising the state machine, analog circuitry, digitalcircuitry, and/or logic circuitry. Still further note that, the memoryelement may store, and the processing module, module, processingcircuit, and/or processing unit executes, hard coded and/or operationalinstructions corresponding to at least some of the steps and/orfunctions illustrated in one or more of the figures. Such a memorydevice or memory element can be included in an article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, and/or any device thatstores digital information. The memory device may be in a form a solidstate memory, a hard drive memory, cloud memory, thumb drive, servermemory, computing device memory, and/or other physical medium forstoring digital information.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A computing device comprising: an interfaceconfigured to interface and communicate with a dispersed or distributedstorage network (DSN); memory that stores operational instructions; anda processing module operably coupled to the interface and to the memory,wherein the processing module, when operable within the computing devicebased on the operational instructions, is configured to: determinecapability levels of a plurality of other computing devices; select,based on the capability levels of a plurality of other computingdevices, a subset of the plurality of other computing devices to performa computing task on a data object, wherein the data object is segmentedinto a plurality of data segments, wherein a data segment of theplurality of data segments is dispersed error encoded in accordance withdispersed error encoding parameters to produce a set of encoded dataslices (EDSs), wherein the set of EDSs are distributedly stored among aplurality of storage units (SUs), wherein a decode threshold number ofEDSs are needed to recover the data segment, wherein a read thresholdnumber of EDSs provides for reconstruction of the data segment, andwherein a write threshold number of EDSs provides for a successfultransfer of the set of EDSs from a first at least one location in theDSN to a second at least one location in the DSN; determine processingparameters of the data based on a number of the subset of the pluralityof other computing devices; determine task partitioning based on thesubset of the plurality of other computing devices, the processingparameters, and a threshold computing parameter; process the data basedon processing parameters to generate data slice groupings; partition thetask based on the task partitioning to generate partial tasks; transmitthe partial tasks and the data slice groupings respectively to thesubset of the plurality of other computing devices to be executedrespectively by the subset of the plurality of other computing devicesto generate a plurality of partial results; when the decode thresholdnumber of the plurality of partial results is generated by the subset ofthe plurality of other computing devices and available as indicated byat least the write threshold number of the subset of the plurality ofother computing devices, obtain at least the decode threshold number ofthe plurality of partial results; and process the at least the decodethreshold number of the plurality of partial results to generate aresult.
 2. The computing device of claim 1, wherein the processingmodule, when operable within the computing device based on theoperational instructions, is further configured to: select, based on thecapability levels of a plurality of other computing devices, the subsetof the plurality of other computing devices to perform a computing taskon the data object based on at least one of comparing an amount of dataassociated with the data object received to a data threshold, a partialtask type, task execution resource availability, or a task schedule. 3.The computing device of claim 1, wherein the threshold computingparameter includes at least one of a decode threshold number ofcomputing devices, a pillar width number of computing devices, or a taskredundancy requirement number of computing devices to execute anidentical partial task.
 4. The computing device of claim 1, wherein theprocessing module, when operable within the computing device based onthe operational instructions, is further configured to: determinewhether the decode threshold number of the plurality of partial resultsis generated by the subset of the plurality of other computing devicesand available based on at least one of receiving a partial result fromat least one of the subset of the plurality of other computing devices,receiving a partial result status from the at least one of the subset ofthe plurality of other computing devices, a query operation to the atleast one of the subset of the plurality of other computing devices,retrieving a partial result from the at least one of the subset of theplurality of other computing devices, or a comparison of a number ofpartial results to the decode threshold number.
 5. The computing deviceof claim 1, wherein the processing module, when operable within thecomputing device based on the operational instructions, is furtherconfigured to: decode the at least the decode threshold number of theplurality of partial results to generate the result.
 6. The computingdevice of claim 1, wherein the computing device is located at a firstpremises that is remotely located from at least one SU of the pluralityof SUs within the DSN.
 7. The computing device of claim 1 furthercomprising: a SU of the plurality of SUs within the DSN, a wirelesssmart phone, a laptop, a tablet, a personal computers (PC), a workstation, or a video game device.
 8. The computing device of claim 1,wherein the DSN includes at least one of a wireless communicationsystem, a wire lined communication systems, a non-public intranetsystem, a public internet system, a local area network (LAN), or a widearea network (WAN).
 9. A computing device comprising: an interfaceconfigured to interface and communicate with a dispersed or distributedstorage network (DSN); memory that stores operational instructions; anda processing module operably coupled to the interface and to the memory,wherein the processing module, when operable within the computing devicebased on the operational instructions, is configured to: determinecapability levels of a plurality of other computing devices; select,based on the capability levels of a plurality of other computingdevices, a subset of the plurality of other computing devices to performa computing task on a data object, wherein the data object is segmentedinto a plurality of data segments, wherein a data segment of theplurality of data segments is dispersed error encoded in accordance withdispersed error encoding parameters to produce a set of encoded dataslices (EDSs), wherein the set of EDSs are distributedly stored among aplurality of storage units (SUs), wherein a decode threshold number ofEDSs are needed to recover the data segment, wherein a read thresholdnumber of EDSs provides for reconstruction of the data segment, andwherein a write threshold number of EDSs provides for a successfultransfer of the set of EDSs from a first at least one location in theDSN to a second at least one location in the DSN; determine processingparameters of the data based on a number of the subset of the pluralityof other computing devices; determine task partitioning based on thesubset of the plurality of other computing devices, the processingparameters, and a threshold computing parameter, wherein the thresholdcomputing parameter includes at least one of a decode threshold numberof computing devices, a pillar width number of computing devices, or atask redundancy requirement number of computing devices to execute anidentical partial task; process the data based on processing parametersto generate data slice groupings; partition the task based on the taskpartitioning to generate partial tasks; transmit the partial tasks andthe data slice groupings respectively to the subset of the plurality ofother computing devices to be executed respectively by the subset of theplurality of other computing devices to generate a plurality of partialresults; when the decode threshold number of the plurality of partialresults is generated by the subset of the plurality of other computingdevices and available as indicated by at least the write thresholdnumber of the subset of the plurality of other computing devices, obtainat least the decode threshold number of the plurality of partialresults; and decode the at least the decode threshold number of theplurality of partial results to generate a result.
 10. The computingdevice of claim 9, wherein the processing module, when operable withinthe computing device based on the operational instructions, is furtherconfigured to: select, based on the capability levels of a plurality ofother computing devices, the subset of the plurality of other computingdevices to perform a computing task on the data object based on at leastone of comparing an amount of data associated with the data objectreceived to a data threshold, a partial task type, task executionresource availability, or a task schedule.
 11. The computing device ofclaim 9, wherein the processing module, when operable within thecomputing device based on the operational instructions, is furtherconfigured to: determine whether the decode threshold number of theplurality of partial results is generated by the subset of the pluralityof other computing devices and available based on at least one ofreceiving a partial result from at least one of the subset of theplurality of other computing devices, receiving a partial result statusfrom the at least one of the subset of the plurality of other computingdevices, a query operation to the at least one of the subset of theplurality of other computing devices, retrieving a partial result fromthe at least one of the subset of the plurality of other computingdevices, or a comparison of a number of partial results to the decodethreshold number.
 12. The computing device of claim 9 furthercomprising: a SU of the plurality of SUs within the DSN, a wirelesssmart phone, a laptop, a tablet, a personal computers (PC), a workstation, or a video game device.
 13. The computing device of claim 9,wherein the DSN includes at least one of a wireless communicationsystem, a wire lined communication systems, a non-public intranetsystem, a public internet system, a local area network (LAN), or a widearea network (WAN).
 14. A method for execution by a computing device,the method comprising: determining capability levels of a plurality ofother computing devices; selecting, based on the capability levels of aplurality of other computing devices, a subset of the plurality of othercomputing devices to perform a computing task on a data object, whereinthe data object is segmented into a plurality of data segments, whereina data segment of the plurality of data segments is dispersed errorencoded in accordance with dispersed error encoding parameters toproduce a set of encoded data slices (EDSs), wherein the set of EDSs aredistributedly stored among a plurality of storage units (SUs), wherein adecode threshold number of EDSs are needed to recover the data segment,wherein a read threshold number of EDSs provides for reconstruction ofthe data segment, and wherein a write threshold number of EDSs providesfor a successful transfer of the set of EDSs from a first at least onelocation in a dispersed or distributed storage network (DSN) to a secondat least one location in the DSN; determining processing parameters ofthe data based on a number of the subset of the plurality of othercomputing devices; determining task partitioning based on the subset ofthe plurality of other computing devices, the processing parameters, anda threshold computing parameter; processing the data based on processingparameters to generate data slice groupings; partitioning the task basedon the task partitioning to generate partial tasks; transmitting, via aninterface configured to interface and communicate with the DSN, thepartial tasks and the data slice groupings respectively to the subset ofthe plurality of other computing devices to be executed respectively bythe subset of the plurality of other computing devices to generate aplurality of partial results; when the decode threshold number of theplurality of partial results is generated by the subset of the pluralityof other computing devices and available as indicated by at least thewrite threshold number of the subset of the plurality of other computingdevices, obtaining at least the decode threshold number of the pluralityof partial results; and processing the at least the decode thresholdnumber of the plurality of partial results to generate a result.
 15. Themethod of claim 14 further comprising: selecting, based on thecapability levels of a plurality of other computing devices, the subsetof the plurality of other computing devices to perform a computing taskon the data object based on at least one of comparing an amount of dataassociated with the data object received to a data threshold, a partialtask type, task execution resource availability, or a task schedule. 16.The method of claim 14, wherein the threshold computing parameterincludes at least one of a decode threshold number of computing devices,a pillar width number of computing devices, or a task redundancyrequirement number of computing devices to execute an identical partialtask.
 17. The method of claim 14 further comprising: determining whetherthe decode threshold number of the plurality of partial results isgenerated by the subset of the plurality of other computing devices andavailable based on at least one of receiving a partial result from atleast one of the subset of the plurality of other computing devices,receiving a partial result status from the at least one of the subset ofthe plurality of other computing devices, a query operation to the atleast one of the subset of the plurality of other computing devices,retrieving a partial result from the at least one of the subset of theplurality of other computing devices, or a comparison of a number ofpartial results to the decode threshold number.
 18. The method of claim14 further comprising: decoding the at least the decode threshold numberof the plurality of partial results to generate the result.
 19. Themethod of claim 14, wherein the computing device is a SU of theplurality of SUs within the DSN, a wireless smart phone, a laptop, atablet, a personal computers (PC), a work station, or a video gamedevice.
 20. The method of claim 14, wherein the DSN includes at leastone of a wireless communication system, a wire lined communicationsystems, a non-public intranet system, a public internet system, a localarea network (LAN), or a wide area network (WAN).